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<a href="_multi_task_svm_8cpp.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span> </div>
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="preprocessor">#include &lt;<a class="code" href="_c_svm_trainer_8h.html">shark/Algorithms/Trainers/CSvmTrainer.h</a>&gt;</span></div>
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="preprocessor">#include &lt;<a class="code" href="_gaussian_rbf_kernel_8h.html">shark/Models/Kernels/GaussianRbfKernel.h</a>&gt;</span></div>
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="preprocessor">#include &lt;<a class="code" href="_multi_task_kernel_8h.html">shark/Models/Kernels/MultiTaskKernel.h</a>&gt;</span></div>
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="preprocessor">#include &lt;<a class="code" href="_zero_one_loss_8h.html">shark/ObjectiveFunctions/Loss/ZeroOneLoss.h</a>&gt;</span></div>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span><span class="preprocessor">#include &lt;<a class="code" href="_data_distribution_8h.html">shark/Data/DataDistribution.h</a>&gt;</span></div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span> </div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="keyword">using namespace </span><a class="code hl_namespace" href="namespaceshark.html" title="AbstractMultiObjectiveOptimizer.">shark</a>;</div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="keyword">using namespace </span>std;</div>
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span> </div>
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span> </div>
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="comment">// RealVector input with task index</span></div>
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno"><a class="line" href="_multi_task_svm_8cpp.html#a0dea9a3a85d327080d9b617903508925">   13</a></span><span class="keyword">typedef</span> <a class="code hl_struct" href="structshark_1_1_multi_task_sample.html" title="Aggregation of input data and task index.">MultiTaskSample&lt;RealVector&gt;</a> <a class="code hl_typedef" href="_multi_task_svm_8cpp.html#a0dea9a3a85d327080d9b617903508925">InputType</a>;</div>
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span> </div>
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span> </div>
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span><span class="comment">// Multi-task problem with up to three tasks.</span></div>
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span><span class="keyword">class </span>MultiTaskProblem : <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_labeled_data_distribution.html" title="A LabeledDataDistribution defines a supervised learning problem.">LabeledDataDistribution</a>&lt;InputType, unsigned int&gt;</div>
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span>{</div>
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span><span class="keyword">public</span>:</div>
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span>    MultiTaskProblem()</div>
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span>    {</div>
<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span>        m_task[0] = <span class="keyword">true</span>;</div>
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span>        m_task[1] = <span class="keyword">true</span>;</div>
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span>        m_task[2] = <span class="keyword">true</span>;</div>
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span>    }</div>
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span> </div>
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span>    <span class="keywordtype">void</span> setTasks(<span class="keywordtype">bool</span> task0, <span class="keywordtype">bool</span> task1, <span class="keywordtype">bool</span> task2)</div>
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span>    {</div>
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span>        m_task[0] = task0;</div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span>        m_task[1] = task1;</div>
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span>        m_task[2] = task2;</div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span>    }</div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span> </div>
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_labeled_data_distribution.html#ace4a2b81e54f0944d241b92a54f1a4c2" title="Generates a single pair of input and label.">draw</a>(<a class="code hl_struct" href="structshark_1_1_multi_task_sample.html" title="Aggregation of input data and task index.">InputType</a>&amp; input, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&amp; label)<span class="keyword"> const</span></div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span><span class="keyword">    </span>{</div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span>        <span class="keywordtype">size_t</span> taskindex = 0;</div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span>        <span class="keywordflow">do</span> {</div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span>            taskindex = <a class="code hl_function" href="namespaceshark_1_1random.html#a18f302ea18f70835c59935973ba8ea84" title="Draws a number uniformly in [lower,upper] by drawing random numbers from rng.">random::uni</a>(<a class="code hl_variable" href="namespaceshark_1_1random.html#ab5c1547eee483974d008d43f621a2234">random::globalRng</a>, 0, 2);</div>
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span>        } <span class="keywordflow">while</span> (! m_task[taskindex]);</div>
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span>        <span class="keywordtype">double</span> x1 = <a class="code hl_function" href="namespaceshark_1_1random.html#a972c5f7f031612a130aa077fc9136a9f" title="Draws a number from the normal distribution with given mean and variance by drawing random numbers fr...">random::gauss</a>(<a class="code hl_variable" href="namespaceshark_1_1random.html#ab5c1547eee483974d008d43f621a2234">random::globalRng</a>);</div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span>        <span class="keywordtype">double</span> x2 = 3.0 * <a class="code hl_function" href="namespaceshark_1_1random.html#a972c5f7f031612a130aa077fc9136a9f" title="Draws a number from the normal distribution with given mean and variance by drawing random numbers fr...">random::gauss</a>(<a class="code hl_variable" href="namespaceshark_1_1random.html#ab5c1547eee483974d008d43f621a2234">random::globalRng</a>);</div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span>        <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> y = (x1 &gt; 0.0) ? 1 : 0;</div>
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span>        <span class="keywordtype">double</span> alpha = 0.05 * M_PI * taskindex;</div>
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span>        input.<a class="code hl_variable" href="structshark_1_1_multi_task_sample.html#a6ddde745da223186836b1286290e750f" title="input data">input</a>.resize(2);</div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span>        input.<a class="code hl_variable" href="structshark_1_1_multi_task_sample.html#a6ddde745da223186836b1286290e750f" title="input data">input</a>(0) = cos(alpha) * x1 - sin(alpha) * x2;</div>
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span>        input.<a class="code hl_variable" href="structshark_1_1_multi_task_sample.html#a6ddde745da223186836b1286290e750f" title="input data">input</a>(1) = sin(alpha) * x1 + cos(alpha) * x2;</div>
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span>        input.<a class="code hl_variable" href="structshark_1_1_multi_task_sample.html#a21d7d05fb180df274599ea6a9cd510a7" title="task index">task</a> = taskindex;</div>
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span>        label = y;</div>
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span>    }</div>
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span> </div>
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span><span class="keyword">protected</span>:</div>
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span>    <span class="keywordtype">bool</span> m_task[3];</div>
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span>};</div>
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span> </div>
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span> </div>
<div class="foldopen" id="foldopen00056" data-start="{" data-end="}">
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno"><a class="line" href="_multi_task_svm_8cpp.html#a3c04138a5bfe5d72780bb7e82a18e627">   56</a></span><span class="keywordtype">int</span> <a class="code hl_function" href="_datasets_8cpp.html#ae66f6b31b5ad750f1fe042a706a4e3d4">main</a>(<span class="keywordtype">int</span> argc, <span class="keywordtype">char</span>** argv)</div>
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span>{</div>
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span>    <span class="comment">// experiment settings</span></div>
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span>    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> ell_train = 1000;      <span class="comment">// number of training data point from tasks 0 and 1</span></div>
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span>    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> ell_test = 1000;       <span class="comment">// number of test data points from task 2</span></div>
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span>    <span class="keywordtype">double</span> C = 1.0;                         <span class="comment">// regularization parameter</span></div>
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span>    <span class="keywordtype">double</span> gamma = 0.5;                 <span class="comment">// kernel bandwidth parameter</span></div>
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span> </div>
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span>    <span class="comment">// generate data</span></div>
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span>    MultiTaskProblem problem;</div>
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span>    problem.setTasks(<span class="keyword">true</span>, <span class="keyword">true</span>, <span class="keyword">false</span>);</div>
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span>    <a class="code hl_class" href="classshark_1_1_labeled_data.html" title="Data set for supervised learning.">LabeledData&lt;InputType, unsigned int&gt;</a> training = problem.generateDataset(ell_train);</div>
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno">   68</span>    problem.setTasks(<span class="keyword">false</span>, <span class="keyword">false</span>, <span class="keyword">true</span>);</div>
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span>    <a class="code hl_class" href="classshark_1_1_labeled_data.html" title="Data set for supervised learning.">LabeledData&lt;InputType, unsigned int&gt;</a> test = problem.generateDataset(ell_test);</div>
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span> </div>
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span>    <span class="comment">// merge all inputs into a single data object</span></div>
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno">   72</span>    <a class="code hl_class" href="classshark_1_1_data.html" title="Data container.">Data&lt;InputType&gt;</a> data(ell_train + ell_test);</div>
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno">   73</span>    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i=0; i&lt;ell_train; i++) </div>
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno">   74</span>        data.<a class="code hl_function" href="group__shark__globals.html#ga0ea72a74a21d5ff59772516b83c4a58b">element</a>(i) = training.<a class="code hl_function" href="group__shark__globals.html#ga6f74e657c7e0c8a32b2456fb328bd653" title="Access to inputs as a separate container.">inputs</a>().<a class="code hl_function" href="group__shark__globals.html#ga0ea72a74a21d5ff59772516b83c4a58b">element</a>(i);</div>
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno">   75</span>    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i=0; i&lt;ell_test; i++) </div>
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno">   76</span>        data.<a class="code hl_function" href="group__shark__globals.html#ga0ea72a74a21d5ff59772516b83c4a58b">element</a>(ell_train + i) = test.<a class="code hl_function" href="group__shark__globals.html#ga6f74e657c7e0c8a32b2456fb328bd653" title="Access to inputs as a separate container.">inputs</a>().<a class="code hl_function" href="group__shark__globals.html#ga0ea72a74a21d5ff59772516b83c4a58b">element</a>(i);</div>
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno">   77</span> </div>
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span>    <span class="comment">// create kernel objects</span></div>
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno">   79</span>    <a class="code hl_class" href="classshark_1_1_gaussian_rbf_kernel.html" title="Gaussian radial basis function kernel.">GaussianRbfKernel&lt;RealVector&gt;</a> inputKernel(gamma);   <span class="comment">// Gaussian kernel on inputs</span></div>
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span>    <a class="code hl_class" href="classshark_1_1_gaussian_task_kernel.html" title="Special &quot;Gaussian-like&quot; kernel function on tasks.">GaussianTaskKernel&lt;RealVector&gt;</a> taskKernel(          <span class="comment">// task similarity kernel</span></div>
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno">   81</span>            data,         <span class="comment">// all inputs with task indices, no labels</span></div>
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span>            3,            <span class="comment">// total number of tasks</span></div>
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span>            inputKernel,  <span class="comment">// base kernel for input similarity</span></div>
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span>            gamma);       <span class="comment">// bandwidth for task similarity kernel</span></div>
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span>    <a class="code hl_class" href="classshark_1_1_multi_task_kernel.html" title="Special kernel function for multi-task and transfer learning.">MultiTaskKernel&lt;RealVector&gt;</a> multiTaskKernel(&amp;inputKernel, &amp;taskKernel);</div>
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno">   86</span> </div>
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span>    <span class="comment">// train the SVM</span></div>
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno">   88</span>    <a class="code hl_struct" href="structshark_1_1_kernel_classifier.html" title="Linear classifier in a kernel feature space.">KernelClassifier&lt;InputType&gt;</a> ke;</div>
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span>    <a class="code hl_class" href="classshark_1_1_c_svm_trainer.html" title="Training of C-SVMs for binary classification.">CSvmTrainer&lt;InputType&gt;</a> trainer(&amp;multiTaskKernel, C,<span class="keyword">false</span>);</div>
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span>    cout &lt;&lt; <span class="stringliteral">&quot;training ...&quot;</span> &lt;&lt; endl;</div>
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span>    trainer.<a class="code hl_function" href="classshark_1_1_c_svm_trainer.html#a9e801518bfba9d02e0749181a5deb0fc" title="Train the C-SVM.">train</a>(ke, training);</div>
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span>    cout &lt;&lt; <span class="stringliteral">&quot;done.&quot;</span> &lt;&lt; endl;</div>
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span> </div>
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno">   94</span>    <a class="code hl_class" href="classshark_1_1_zero_one_loss.html" title="0-1-loss for classification.">ZeroOneLoss&lt;unsigned int&gt;</a> loss;</div>
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span>    <a class="code hl_class" href="classshark_1_1_data.html" title="Data container.">Data&lt;RealVector&gt;</a> output;</div>
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span> </div>
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno">   97</span>    <span class="comment">// evaluate training performance</span></div>
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno">   98</span>    <span class="keywordtype">double</span> trainError = loss.<a class="code hl_function" href="classshark_1_1_zero_one_loss.html#acba6670d53701d50eed0ecdbc1114175" title="Return zero if labels == predictions and one otherwise.">eval</a>(training.<a class="code hl_function" href="group__shark__globals.html#ga6328a5aa2570c01a5ac5f25076071663" title="Access to labels as a separate container.">labels</a>(), ke(training.<a class="code hl_function" href="group__shark__globals.html#ga6f74e657c7e0c8a32b2456fb328bd653" title="Access to inputs as a separate container.">inputs</a>()));</div>
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno">   99</span>    cout &lt;&lt; <span class="stringliteral">&quot;training error:\t&quot;</span> &lt;&lt;  trainError &lt;&lt; endl;</div>
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno">  100</span> </div>
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span>    <span class="comment">// evaluate its transfer performance</span></div>
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span>    <span class="keywordtype">double</span> testError = loss.<a class="code hl_function" href="classshark_1_1_zero_one_loss.html#acba6670d53701d50eed0ecdbc1114175" title="Return zero if labels == predictions and one otherwise.">eval</a>(test.<a class="code hl_function" href="group__shark__globals.html#ga6328a5aa2570c01a5ac5f25076071663" title="Access to labels as a separate container.">labels</a>(), ke(test.<a class="code hl_function" href="group__shark__globals.html#ga6f74e657c7e0c8a32b2456fb328bd653" title="Access to inputs as a separate container.">inputs</a>()));</div>
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span>    cout &lt;&lt; <span class="stringliteral">&quot;test error:\t&quot;</span> &lt;&lt; testError &lt;&lt; endl;</div>
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span>}</div>
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